SoroushVahidi/scidocs huggingface dataset:
Semantic Audiovisual Data Fusion for Automatic Emotion Recognition
(2015)

The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal semantic data fusion model that determines the most probable emotion of the subject. Two types of models based on geometric face features for facial expression recognition are being used, depending on the presence or absence of speech. In our approach we define an algorithm that is robust to changes of face shape that occur during regular speech. The influence of phoneme generation on the face shape during speech is removed by using features that are only related to the eyes and the eyebrows. The paper includes results from testing the presented models.
Mobile Food Recognition with an Extreme Deep Tree
(2016)

Food recognition is an emerging topic in the field of computer vision. The recent interest of the research community in this area is justified by the rise in popularity of food diary applications, where the users take note of their food intake for self-monitoring or to provide useful statistics to dietitians. However, manually annotating food intake can be a tedious task, thus explaining the need of a system that automatically recognizes food, and possibly its amount, from pictures acquired by mobile devices. In this work we propose an approach to food recognition which combines the strengths of different state-of-the-art classifiers, namely Convolutional Neural Networks, Extreme Learning Machines and Neural Trees. We show that the proposed architecture can achieve good results even with low computational power, as in the case of mobile devices.
Efficient Selection of Vector Instructions Using Dynamic Programming
(2010)

Accelerating program performance via SIMD vector units is very common in modern processors, as evidenced by the use of SSE, MMX, VSE, and VSX SIMD instructions in multimedia, scientific, and embedded applications. To take full advantage of the vector capabilities, a compiler needs to generate efficient vector code automatically. However, most commercial and open-source compilers fall short of using the full potential of vector units, and only generate vector code for simple innermost loops. In this paper, we present the design and implementation of anauto-vectorization framework in the back-end of a dynamic compiler that not only generates optimized vector code but is also well integrated with the instruction scheduler and register allocator. The framework includes a novel{\em compile-time efficient dynamic programming-based} vector instruction selection algorithm for straight-line code that expands opportunities for vectorization in the following ways: (1) {\em scalar packing} explores opportunities of packing multiple scalar variables into short vectors, (2)judicious use of {\em shuffle} and {\em horizontal} vector operations, when possible, and (3) {\em algebraic reassociation} expands opportunities for vectorization by algebraic simplification. We report performance results on the impact of auto-vectorization on a set of standard numerical benchmarks using the Jikes RVM dynamic compilation environment. Our results show performance improvement of up to 57.71\% on an Intel Xeon processor, compared tonon-vectorized execution, with a modest increase in compile-time in the range from 0.87\% to 9.992\%. An investigation of the SIMD parallelization performed by v11.1 of the Intel Fortran Compiler (IFC) on three benchmarks shows that our system achieves speedup with vectorization in all three cases and IFC does not. Finally, a comparison of our approach with an implementation of the Super word Level Parallelization (SLP) algorithm from~\cite{larsen00}, shows that our approach yields a performance improvement of up to 13.78\% relative to SLP.
Body Part Detection for Human Pose Estimation and Tracking
(2007)

Accurate 3-D human body pose tracking from a monocular video stream is important for a number of applications. We describe a novel hierarchical approach for tracking human pose that uses edge-based features during the coarse stage and later other features for global optimization. At first, humans are detected by motion and tracked by fitting an ellipse in the image. Then, body components are found using edge features and used to estimate the 2D positions of the body joints accurately. This helps to bootstrap the estimation of 3D pose using a sampling-based search method in the last stage. We present experiment results with sequences of different realistic scenes to illustrate the performance of the method.
The American Sign Language Lexicon Video Dataset
(2008)

The lack of a written representation for American sign language (ASL) makes it difficult to do something as commonplace as looking up an unknown word in a dictionary. The majority of printed dictionaries organize ASL signs (represented in drawings or pictures) based on their nearest English translation; so unless one already knows the meaning of a sign, dictionary look-up is not a simple proposition. In this paper we introduce the ASL lexicon video dataset, a large and expanding public dataset containing video sequences of thousands of distinct ASL signs, as well as annotations of those sequences, including start/end frames and class label of every sign. This dataset is being created as part of a project to develop a computer vision system that allows users to look up the meaning of an ASL sign. At the same time, the dataset can be useful for benchmarking a variety of computer vision and machine learning methods designed for learning and/or indexing a large number of visual classes, and especially approaches for analyzing gestures and human communication.
MHz-frequency operation of flyback converter with monolithic self-synchronized rectifier (SSR)
(2010)

Synchronous rectification offers an effective solution to improve efficiency of low-output-voltage flyback converters. Conventional synchronous flyback topologies using discrete synchronous rectifier face challenges in precise timing control on the secondary side. In this paper, MHz-frequency operation of a flyback DC-DC converter using a new monolithic self-synchronized rectifier (SSR) is investigated and demonstrated. The SSR IC, acting solely on its own drain-source voltage instead of external control signals, considerably simplifies converter design, improves system efficiency, and enables MHz operating frequency. Analysis, modeling, and experimental results are presented.
Assessing The Feasibility Of Self-organizing Maps For Data Mining Financial Information
(2002)

Analyzing financial performance in today’s information-rich society can be a daunting task. With the evolution of the Internet, access to massive amounts of financial data, typically in the form of financial statements, is widespread. Managers and stakeholders are in need of a data-mining tool allowing them to quickly and accurately analyze this data. An emerging technique that may be suited for this application is the self-organizing map. The purpose of this study was to evaluate the performance of self-organizing maps for analyzing financial performance of international pulp and paper companies. For the study, financial data, in the form of seven financial ratios, was collected, using the Internet as the primary source of information. A total of 77 companies, and six regional averages, were included in the study. The time frame of the study was the period 1995-00. An example analysis was performed, and the results analyzed based on information contained in the annual reports. The results of the study indicate that self-organizing maps can be feasible tools for the financial analysis of large amounts of financial data.
A Self-Cloning Agents Based Model for High-Performance Mobile-Cloud Computing
(2015)

The rise of the mobile-cloud computing paradigm in recent years has enabled mobile devices with processing power and battery life limitations to achieve complex tasks in real-time. While mobile-cloud computing is promising to overcome the limitations of mobile devices for real-time computing, the lack of frameworks compatible with standard technologies and techniques for dynamic performance estimation and program component relocation makes it harder to adopt mobile-cloud computing at large. Most of the available frameworks rely on strong assumptions such as the availability of a full clone of the application code and negligible execution time in the cloud. In this paper, we present a dynamic computation offloading model for mobile-cloud computing, based on autonomous agents. Our approach does not impose any requirements on the cloud platform other than providing isolated execution containers, and it alleviates the management burden of offloaded code by the mobile platform using stateful, autonomous application partitions. We also investigate the effects of different cloud runtime environment conditions on the performance of mobile-cloud computing, and present a simple and low-overhead dynamic make span estimation model integrated into autonomous agents to enhance them with self-performance evaluation in addition to self-cloning capabilities. The proposed performance profiling model is used in conjunction with a cloud resource optimization scheme to ensure optimal performance. Experiments with two mobile applications demonstrate the effectiveness of the proposed approach for high-performance mobile-cloud computing.
QUBIC: a qualitative biclustering algorithm for analyses of gene expression data
(2009)

Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called 'scaling patterns', a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/ approximately maqin/bicluster. A server version of QUBIC is also available upon request.
Autonomous Inverted Helicopter Flight via Reinforcement Learning
(2004)

Helicopters have highly stochastic, nonlinear, dynamics, and autonomous helicopter flight is widely regarded to be a challenging control problem. As helicopters are highly unstable at low speeds, it is particularly difficult to design controllers for low speed aerobatic maneuvers. In this paper, we describe a successful application of reinforcement learning to designing a controller for sustained inverted flight on an autonomous helicopter. Using data collected from the helicopter in flight, we began by learning a stochastic, nonlinear model of the helicopter’s dynamics. Then, a reinforcement learning algorithm was applied to automatically learn a controller for autonomous inverted hovering. Finally, the resulting controller was successfully tested on our autonomous helicopter platform.
Dynamic Programming and Optimal Control
(1995)

This is an updated version of Chapter 4 of the author’s Dynamic Programming and Optimal Control, Vol. II, 4th Edition, Athena Scientific, 2012. It includes new material, and it is substantially revised and expanded (it has more than doubled in size). The new material aims to provide a unified treatment of several models, all of which lack the contractive structure that is characteristic of the discounted problems of Chapters 1 and 2: positive and negative cost models, deterministic optimal control (including adaptive DP), stochastic shortest path models, and risk-sensitive models. Here is a summary of the new material:
On-line Q-learning using connectionist systems
(1994)

Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete nite-state Markovian problems, which is too restrictive for many real-world environments. Therefore, it is desirable to extend these methods to high dimensional continuous state-spaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of back-propagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number of di erent algorithms based around Q-Learning (Watkins 1989) combined with the Temporal Di erence algorithm (Sutton 1988), including a new algorithm (Modi ed Connectionist Q-Learning), and Q( ) (Peng and Williams 1994). In addition, we present algorithms for applying these updates on-line during trials, unlike backward replay used by Lin (1993) that requires waiting until the end of each trial before updating can occur. On-line updating is found to be more robust to the choice of training parameters than backward replay, and also enables the algorithms to be used in continuously operating systems where no end of trial conditions occur. We compare the performance of these algorithms on a realistic robot navigation problem, where a simulated mobile robot is trained to guide itself to a goal position in the presence of obstacles. The robot must rely on limited sensory feedback from its surroundings, and make decisions that can be generalised to arbitrary layouts of obstacles. These simulations show that on-line learning algorithms are less sensitive to the choice of training parameters than backward replay, and that the alternative update rules of MCQ-L and Q( ) are more robust than standard Q-learning updates. 1
Autonomous Helicopter Flight via Reinforcement Learning
(2003)

Autonomous helicopter flight represents a challenging cont rol problem, with complex, noisy, dynamics. In this paper, we describe a s uccessful application of reinforcement learning to autonomous helic opter flight. We first fit a stochastic, nonlinear model of the helicopter dy namics. We then use the model to learn to hover in place, and to fly a number of maneuvers taken from an RC helicopter competition.
Latent syntactic structure-based sentiment analysis
(2017)

People share their opinions about things like products, movies and services using social media channels. The analysis of these textual contents for sentiments is a gold mine for marketing experts, thus automatic sentiment analysis is a popular area of applied artificial intelligence. We propose a latent syntactic structure-based approach for sentiment analysis which requires only sentence-level polarity labels for training. Our experiments on three domains (movie, IT products, restaurant) show that a sentiment analyzer that exploits syntactic parses and has access only to sentence-level polarity annotation for in-domain sentences can outperform state-of-the-art models that were trained on out-domain parse trees with sentiment annotation for each node of the trees. In practice, millions of sentence-level polarity annotations are usually available for a particular domain thus our approach is applicable for training a sentiment analyzer for a new domain while it can exploit the syntactic structure of sentences as well.
Boosting the Quality of Approximate String Matching by Synonyms
(2015)

A string-similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings “Sam” and “Samuel” can be considered to be similar. Most existing work that computes the similarity of two strings only considers syntactic similarities, for example, number of common words or q-grams. While this is indeed an indicator of similarity, there are many important cases where syntactically-different strings can represent the same real-world object. For example, “Bill” is a short form of “William,” and “Database Management Systems” can be abbreviated as “DBMS.” Given a collection of predefined synonyms, the purpose of this article is to explore such existing knowledge to effectively evaluate the similarity between two strings and efficiently perform similarity searches and joins, thereby boosting the quality of approximate string matching.
 In particular, we first present an expansion-based framework to measure string similarities efficiently while considering synonyms. We then study efficient algorithms for similarity searches and joins by proposing two novel indexes, called SI-trees and QP-trees, which combine signature-filtering and length-filtering strategies. In order to improve the efficiency of our algorithms, we develop an estimator to estimate the size of candidates to enable an online selection of signature filters. This estimator provides strong low-error, high-confidence guarantees while requiring only logarithmic space and time costs, thus making our method attractive both in theory and in practice. Finally, the experimental results from a comprehensive study of the algorithms with three real datasets verify the effectiveness and efficiency of our approaches.
Augmented Reality with Hololens: Experiential Architectures Embedded in the Real World
(2016)

Early hands-on experiences with the Microsoft Hololens augmented/mixed reality device are reported and discussed, with a general aim of exploring basic 3D visualization. A range of usage cases are tested, including data visualization and immersive data spaces, in-situ visualization of 3D models and full scale architectural form visualization. Ultimately, the Hololens is found to provide a remarkable tool for moving from traditional visualization of 3D objects on a 2D screen, to fully experiential 3D visualizations embedded in the real world.
Aspect based sentiment analysis
(2014)

In this fast paced and social media frenzy world, decision making has been revolutionized as there are lots of opinions floating on the internet in the form of blogs, social media updates, forums etc. This paper focuses on how banks can utilize these reviews to improve their services. The reviews are scraped from the internet and the sentiment analysis is done on an aspect level to have a much clear idea of where the banks are performing bad according to the customers or users.
Real time object detection and tracking using the Kalman Filter embedded in single board in a robot
(2017)

This paper presents an algorithm implemented in real time in a laboratory robot, designed to trace and detect the color of an object in an indoor environment using the Kalman filter. The image capture is done by a kinect camera installed in the robot. The algorithm is programmed into a single board computer, BeagleBone Black that uses the ROS — OpenCV system in image processing.
A Case for Malleable Thread-Level Linear Algebra Libraries: The LU Factorization With Partial Pivoting
(2016)

We propose two novel techniques for overcoming load-imbalance encountered when implementing so-called look-ahead mechanisms in relevant dense matrix factorizations for the solution of linear systems. Both techniques target the scenario where two thread teams are created/activated during the factorization, with each team in charge of performing an independent task/branch of execution. The first technique promotes worker sharing (WS) between the two tasks, allowing the threads of the task that completes first to be reallocated for use by the costlier task. The second technique allows a fast task to alert the slower task of completion, enforcing the early termination (ET) of the second task, and a smooth transition of the factorization procedure into the next iteration. The two mechanisms are instantiated via a new malleable thread-level implementation of the basic linear algebra subprograms, and their benefits are illustrated via an implementation of the LU factorization with partial pivoting enhanced with look-ahead. Concretely, our experimental results on an Intel-Xeon system with 12 cores show the benefits of combining WS+ET, reporting competitive performance in comparison with a task-parallel runtime-based solution.
Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps
(2013)

Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper focuses on a particularly important challenge: mapping in dynamic environments. We introduce several improvements to the recently proposed Normal Distributions Transform Occupancy Map (NDT-OM) aimed for efficient mapping in dynamic environments. A careful consistency analysis is given based on convergence and similarity metrics specifically designed for evaluation of NDT maps in dynamic environments. We show that in the context of mapping with known poses the proposed method results in improved consistency and in superior runtime performance, when compared against 3D occupancy grids at the same size and resolution. Additionally, we demonstrate that NDT-OM features real-time performance in a highly dynamic 3D mapping and tracking scenario with centimeter accuracy over a 1.5km trajectory.
CHAPTER TWO Self-Distancing : Theory , Research , and Current Directions
(2016)

When people experience negative events, they often try to understand their feelings to improve the way they feel. Although engaging in this meaning-making process leads people to feel better at times, it frequently breaks down leading people to ruminate and feel worse. This raises the question: What factors determine whether people’s attempts to “work-through” their negative feelings succeed or fail? In this article, we describe an integrative program of research that has addressed this issue by focusing on the role that self-distancing plays in facilitating adaptive self-reflection. We begin by describing the “self-reflection puzzle” that initially motivated this line of work. Next, we introduce the concept of self-distancing and describe the conceptual framework we developed to explain how this process should facilitate adaptive self-reflection. After describing the early studies that evaluated this framework, we discuss how these findings have been extended to broaden and deepen our understanding of the role that this process plays in self-regulation. We conclude by offering several parting thoughts that integrate the ideas discussed in this chapter. 1. THE SELF-REFLECTION PUZZLE Many people try to understand their feelings when they are upset, under the assumption that doing so will lead them to feel better. Indeed, it would seem that many of us reflexively heed Socrates’ advice to “know thyself ” when we experience emotional pain. But are people’s attempts to work-through their feelings productive? Do they actually lead people to feel better? A great deal of research has addressed these questions over the past 40 years, and the results reveal a puzzle. On the one hand, several studies suggest that it is indeed helpful for people to reflect on their emotions when they experience distress 82 E. Kross and O. Ayduk